| Citation: | ZHU B K，MIN K，WANG J Y，et al. Recognition of hypersonic inlet unstart state based on KMMS-ReliefF and SA-SVMRFE[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（12）：4330-4341 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0655
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Inlet unstart state can seriously affect the normal operation of hypersonic engines. Based on the pattern classification method of steady state pressure information, a hypersonic three-dimensional internal rotary combined inlet is taken as the research object to solve the unstart state recognition problem by extracting the key reliable wall pressure measurement points and constructing a high-accuracy classification model. Firstly, a number of along-track wall pressure data are obtained for several start/unstart states by numerical simulation, under different Mach numbers and back pressure conditions. Secondly, an algorithm integrating ReliefF (KMMS-ReliefF) and k-means clustering is proposed for the construction of the measurement points selection methods; this algorithm solves the imbalance of start/unstart categories of the dataset while completely considering the weight information of the joint feature pairs. Besides, in order to take into account the feature weights and the global classification accuracy, an improved SVM Recursive Feature Elimination Algorithm with simulated annealing strategy is proposed (SA-SVMRFE). Finally, the two algorithms are combined into a two-stage algorithm (KRSAS). A significant number of unnecessary measurement points are swiftly eliminated from the original dataset in the first stage using the KMMS-ReliefF algorithm. In the second stage, the SA-SVMRFE algorithm eliminates the redundant measurement points from the remaining subset of points. Then the comparison is made with the other four combined algorithms. The experimental results show that the combinatorial algorithm proposed in this paper is significantly lower than other algorithms in terms of optimal feature subset dimension. The unstart recognition model trained by 10-fold cross-validation SVM (10-cv SVM) has an average classification accuracy of more than 99% in the test set of each model tunnel, and has high operational efficiency. In addition, other classification algorithms such as kNN and AdaBoost are used to verify the reliability of the optimal measurement point combinations.
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